Abstract
Methods for parallelized eye movement analysis on a cluster are detailed. The distributed approach advocates the single-core job programming strategy, assigning processing of eye movement data across as many cluster cores as are available. A foreman-worker distribution algorithm takes care of job assignment via the Message Passing Interface (MPI) available on most high-performance computing clusters. Two versions of the MPI algorithm are presented, the first a straightforward implementation that assumes faultless operation, the second a more fault-tolerant revision that gives nodes an opportunity of communicating failure. Job scheduling is also briefly explained.
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Duchowski, A.T., Bolte, T., Krejtz, K. (2015). Massive-Scale Gaze Analytics Exploiting High Performance Computing. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_13
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DOI: https://doi.org/10.1007/978-3-319-19857-6_13
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